Hospitalisation expenditure on tuberculosis among tribal populations in India: A repeated cross-sectional analysis of national sample survey data, 2004 to 2018

Objective Tribal population in India (8.6% of the total population) have a greater prevalence of tuberculosis compared to the national average. The article aims to study out-of-pocket expenditure (OOPE), hardship financing, and impoverishment effects of TB hospitalisation treatment among tribal populations in India. Methods Data of three rounds of National Sample Surveys (NSS) 60th (2004–05), 71st (2013–14) and 75th (2017–18) rounds were analyzed. Descriptive statistics, bivariate estimates and multivariate models were performed to calculate the OOPE, healthcare burden (HCB), catastrophic health expenditure (CHE), hardship financing and impoverishment effects using standard definitions at February 2023 price values. Propensity score matching (PSM) was used to examine the effect of health insurance coverage on catastrophic health expenditure, and impoverishment. Results Over two-thirds of the TB cases are seen in the economically productive age group (14–59 years). Substantial OOPE and its impact on HCB, CHE, and poverty impact observed among 15–35 age group across all three rounds. Illiterate patients and those availing private hospitals for TB treatment had higher OOPE, HCB, hardship financing, CHE, and poverty impact. 38.5% (2014) and 33.2% (2018) are covered with any kind of public healthcare coverage, PSM analysis shows households with health insurance have lower incidence of CHE and impoverishment effects due to TB hospitalisation expenditure. Conclusions The current study aids in comprehending the patterns in the financial burden of TB on tribal households during the previous 15 years and gives policy makers information for efficient resource allocation management for TB among Indian tribal communities.


Introduction
Mycobacterium tuberculosis infection causes the highest number of deaths due to infectious diseases in the world [1][2][3].India has always been at the forefront of TB control, yet it still has the highest burden of TB cases (27% of the global share) in the world [4].India also has the highest burden of multi-drug-resistant tuberculosis (MDR-TB) [5][6][7].Additionally, MDR-TB calls for a higher cost of treatment than drug-resistant TB, this adds to the financial burden for TB management [8].
In India, tribal population (110.4 million people comprising of 8.6% of total population) have higher prevalence (703 per 100,000) of TB compared to national average (256 per 100,000) [9].There also exists a wide variation of TB prevalence among the tribal groups at regional and state levels [10].10.4% of all TB notified patients in the country belong to tribal communities [9,11,12].
Since 2005 the National TB program have prioritized tribal subgroup population through Tribal Action Plans [13].This Tribal TB initiative is a unique partnership between the Ministry of Health and Family Welfare and Ministry of Tribal Affairs to improve TB care and support services amongst the tribal populations [13].Despite these efforts, there are still a number of barriers to the prevention and treatment of tuberculosis in tribal populations in the country.These include poverty, malnutrition, illiteracy, alcoholism, substance abuse, poor access to health care, and a variety of socio-cultural and environmental factors that affect the access and duration of TB treatments [11][12][13] There also exist other factors such as rituals, beliefs about herbal medicines, lack of community involvement, barriers in the health system (such a lack of personnel with the right training), and communication and cultural divides between the patient and the caregiver [11][12][13].The implementation of the Revised National Tuberculosis Control Program (RNTCP) has been shown to be hindered by social stigma and a lack of awareness of TB among tribal people [10,14].
Previous studies have highlighted TB to be a major cause of catastrophic health expenditure for the tribal populace in the country.Muniyandi et al. (2020) highlighted the need for the decrease of direct and indirect TB patient costs so as to prevent chances of catastrophic payments to particularly vulnerable tribal groups (PVTGs) [15].Although majority of the PVTGs received treatment free of costs, those who incurred costs had to face catastrophic TB care expenditure amounting to 10% of various costs in relation to annual family income [15].
Studies focusing on healthcare expenditure on TB in India previously have looked at tribal populations however these are limited to a specific geographical area [10,15,16].Other studies that have used nationally representative surveys such as the National Sample Survey (NSS) have included tribal populations but have not described the various socio-economic dimensions, such as age, education, gender, marital status, income quintile etc. [17][18][19].In India, tribal populations have higher prevalence with over 10% of all TB notified patients belonging to tribal communities [10,14,15].The financial impact of hospitalisation related to out-of-pocket expenses (OOPE) caused by tuberculosis (TB) among tribal populations can be influenced by several factors; including literacy, religion, caste, urban/rural residence, type of ward (paying, general, paying special, and free), and length of hospital stay.
Our study is specifically focused on tribal populations in India, across time periods using a repeated cross-sectional analysis of the 60th, 71st and 75th rounds of NSS across socio-economic and demographic characteristics, for TB-related hospitalisation out-of-pocket expenditure (OOPE), catastrophic health expenditure, hardship financing, and impoverishment effects.There is need to highlight the financial impact of hospitalisation and its various socio-economic dimensions related OOPE due to TB among tribal populations for bringing forth attention to this underserved population for the policy makers in the country.

Data and sample size
This study used secondary data from three rounds of NSS (60th round (2004), 71st round (2014) and 75th round (2018)).NSS 60th round data covered 73,868 households and 383,338 individuals, NSS 71st round covered 65,932 households and 333,104 individuals and NSS 75th round covered 1,13,823 households and 5,55,115 individuals.For our analysis, all the tribal population who were hospitalized due to TB (114 in NSS 60th round, 106 in NSS 71st round and 115 in NSS 75th round) in the last one year preceding the survey were included.

Study design
NSS is a cross-sectional, nationally representative, large-scale survey in India which adopts a multi-stage sampling design to select a representative sample of households across all states and union territories in India.In the first stage of the sampling, villages/urban blocks are selected using probability proportional to size with replacement.In the second stage, households are chosen by systematic random sampling without replacement (from selected villages/urban blocks).More ssampling design, data collection, and management procedures are described in the respective NSS reports [20][21][22].

Outcome measurements
For OOPE, this study used direct medical costs (hospital stay, consultation, treatment medicines and procedures, laboratory and other investigation charges), direct non-medical costs (transportation, meals, lodging (for patients and attendants) [16][17][18].NSS captures hospitalisation expenditure for the last 365 days preceding the survey period.Respective sample weights were then applied on hospitalisation expenditure for TB for the calculation of results [16][17][18].
The cost of treatment on TB as a share of total HCE of households and reported as Healthcare Burden was calculated using the formula below [17,18];

Share of OOPE on HCE =
Household's Yearly Health Care Expenditure x 100 Household's Yearly Consumption Expenditure Our study estimates the ratio method for CHE that if OOPE exceeding 10% of total HCE as suggested in previous studies [17][18][19].
Hardship financing is a situation when a household have to borrow money with interest or sell its property/assets to meet its health care expenses [23].
The impoverishment effects of OOPE due to TB was measured using two indices: poverty head count ratio and poverty gap ratio.The poverty gap measures the percentage deficit from the poverty line of those households that have become poor due to OOPE, and poverty gap ratio measures the percentage deficit from the poverty line of households that have become poor due to OOPE as a proportion of all the households in the population [17][18][19].
When HCE is equal or more than poverty line (PL) and after making the OOPE (HCE-OOPE) is less than the poverty line (PL) is called a poverty head count (PHC) [17][18][19].For the poverty line, this study used, per capita monthly expenditure' as ₹972 in rural and ₹1407 in urban according the Rangarajan Committee, 2014 [24].The amount of poverty line is adjusted for inflation to February 2023 using the consumer price index as the Rangarajan Committee recommendation is based on 2012 data.

Poverty head count (PHC
Otherwise 0, where HCE i is monthly consumption expenditure of i th household. Percentage of households who falls below poverty line due to OOPE is defined as poverty head count ratio (PHCR) [17][18][19].

Poverty head count ratio(PHCR)
The poverty gap ratio (PGR) is measured the average percentage deficit from the poverty line due to OOPE [18,19].

Povery gap (PG
Currency exchange rate of 1 USD=Rupee 82.6321 (Average exchange rate in February 2023) was considered for conversion for presentation of results.

Defining predictor variables
Important socioeconomic and demographic predictors such as sex, age, education, caste, religion, income quintile, place of residence, regions of residence, types of health facility used for treatment, types of wards used for treatment and duration of hospitalisation were included as predictor variables in the present study based on the determinants examined in earlier similar studies and available variables in the dataset [17,19].

Analytical approach
Descriptive statistics, bivariate estimates and multivariable models were performed in order to achieve the research objectives.In the first part of the analysis, descriptive analysis was carried out to estimate the mean OOPE and mean yearly consumption expenditure.In the second step of the analysis, bivariate analyses were carried out to understand the differences in health care burden, catastrophic health expenditure, and hardship financing by socioeconomic characteristics of the patient.multivariable linear regression for OOPE (as a continuous variable) and logistic regression analysis for CHE and hardship financing (as a binary variable) were carried out to estimate the adjusted effects of selected covariates for hospitalisation only which used by many previous studies [17,19]; as sample sizes in population utilizing OP care was limited.To take into account the survey design, i.e., sampling weights with clustering and strata while estimating bivariate and multivariable statistics, the SVY command [25] was used in STATA 13 (StataCorp LLP) [26].In order to compare the expenditure of three rounds Consumer Price Index (CPI) has been used, usually termed as "Inflation Rate", which measures and examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care.All the expenditure reported in the year 2004 and 2014 and 2017-18 were further adjusted for inflation to February 2023 using the consumer price index with the following formula [18,19].Propensity score matching (PSM) was used to examine the effect of health insurance coverage on catastrophic health expenditure, and impoverishment [27].In our study, PSM establishes that an intervention (households covered with any health insurance) contributes to an outcome of interest (impact of OOPE that is the incidence of CHE), i.e., 'does household covered by any health insurance has lower incidence of CHE and impoverishment?'.The average treatment on treated (ATT), which measures the average difference in CHE and poverty impact that health insurance schemes afford to households with health care coverage, is a measure of the effectiveness of the health care insurance [27].As NSS 60th round did not report information on health insurance coverage, our analysis focused on NSS 71st round and NSS 75th round.
15-35 years old in tribal households were most exposed to CHE as  7).Percentage of the tribal households falling below the PL and the average percentage deficit from the poverty line (PL) due to OOPE for TB shows substantial increase from 2004 to 2018 (Table 8).Tribal households in urban areas showed a higher percentage of poverty headcount ratio in 2014 compared to 2004 and 2018.Over 50.7% of the tribal households in urban areas seeking private hospitals for treatment fell below the PL in NSS-2018.Patients in paid wards had considerably high percentage of the population falling below the PL across all the three rounds.Those with no healthcare coverage had substantial percentage of population falling below the PL along with average percentage deficit from the PL in 2018 compared to those with public healthcare coverage (Table 8 and S5).
Results from the PSM show that for the unmatched sample estimate, households with health insurance were 11% less likely to experience CHE (at 10% threshold) compared to households that had no insurance coverage in 2014 (Table 9).The calculated ATT values in the treated and control groups were 0.30 and 0.38, respectively, which means that after matching, reduction to CHE (at 10% threshold) was attributed to health insurance coverage by 8% margin.The computed ATU values for the intervention and control groups were 0.41 and 0.46 respectively.This means that the participants who did not have any health insurance, if they were covered by some form of health insurance, the chance of CHE (at 10% threshold) would have reduced by 5%.The 2018 estimates indicate that households with health insurance were 3% less likely to experience CHE (at 10% threshold) compared to households that had no insurance coverage based on computed ATT values.However, the chance of CHE (at 10% threshold) would have increased by 13% between treatment and controls as per computed ATU values (Table 9).
Those households that had health insurance were 7% less likely to be impoverished compared to households that had no health insurance (Table 10) in 2018 according to the unmatched estimate.The calculated ATT values in the treated and control groups were 0.03 and 0.24, respectively, which means that after matching, 21% of the households without any health insurance were impoverished compared to noninsured.The estimates based on a 2014 indicate that 9% of health insurance households pushed on poverty compared to 21% of non-insured households, a difference of 12% which shows that those households that had health insurance were 12% less likely to pushed on poverty compared to households that had no insurance coverage in 2014.

Discussion
The main findings of the study conducted among hospitalized cases for TB in tribal populations are: (a) There is substantial increase in OOPE and healthcare burden due to TB; (b) Utilisation of private hospitals and paid wards lead to higher OOPE, hardship financing, CHE and impoverishment; (c) Households without healthcare coverage were significantly impacted for CHE and impoverishment compared to those with health insurance.
Over two-thirds of the TB cases are seen in the economically productive age group (15-59 years) with high OOPE, and healthcare burden compared to all other age groups across all age-groups.Within this agegroup 15-35 age group shows substantial impact of OOPE, HCB, CHE and poverty impact across all three rounds.These findings are similar to the economic burden due to TB in general population as seen in previous studies [18,19].
Illiterate patients who accessed TB treatment were exposed to higher healthcare burden, catastrophic health expenditure, hardship financing, and impoverishment compared to others due to hospitalisation expenses.According to Census of India 2011 data, the literacy rate among tribal people in India was 59%, which is much lower than the national average (73%) with only some improvement from Census of India 2001 [28].Previous studies have also highlighted poor health literacy among tribal populations in India [29].While efforts for improving education access in the country by the government is underway it may be important it might be worthwhile to improve the health literacy in tribal populations for increasing their understanding of need of care for managing TB.Results from our PSM analysis show that health insurance has a high impact in reducing CHE and impoverishment resulting due to hospitalisation expenditure due to TB.However, almost 61.5% and 66.8% are not covered with any kind of public health insurance scheme as per our analysis.In 2022, the central government has announced the provision of health insurance cover to Renotified, Nomadic and Semi-nomadic trial communities under the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) under the Scheme for Economic Empowerment of De-Notified, Nomadic and Semi Nomadic Tribes (SEED) [30].However, analysis done for the Tribal Health in India Report (2018) mentions about substantial under-spending of the Tribal Sub-Plan (TSP) and low earmarked allocation to tribal health [31].There is a need for faster implementation of this scheme and proper allocation of the TSP to ensure that tribal households receive financial protection to manage TB related expenditure in the future.
Our analysis on CHE using multivariate logistic regression and PSM shows contrasting results.
The Tribal Health in India Report has highlighted the usage of private healthcare facilities among tribal populations in the country [31].Our analysis shows that the use of private healthcare facilities for TB hospitalisation showed a downward trend from 2004 to 2014 (21%-18.7%)but this has increased to 29.7% in 2018.Our analysis also suggests that the use of private healthcare facilities for TB hospitalisation

Table 8
Percentage of tribal households falling below poverty line (poverty headcount ratio) and average percentage deficit from the poverty line (poverty gap ratio) due to OOPE for TB treatment in India (NSS 2004-2018).treatment by tribal populations is substantially lower (29.7%)compared to use of public facilities (70.4%).Additional analysis shows that 31.46% and 28.57% of patients have healthcare coverage to those visiting public and private health facilities respectively.However, this is worrisome, since as per our estimates, patients in private hospitals had substantial OOPE, hardship financing, CHE, and impoverishment.The health outcomes of tribal communities, which enhance the local spread of infectious illnesses like tuberculosis, have been shown to been impacted due to factors related to access to public healthcare services such as distance, and waiting time, in comparison to those living in rural and urban regions [32].Our multivariate analysis found that populations with health insurance were significantly associated with lower OOPE but had higher odds of exposure to hardship financing and CHE (at 10% threshold).Our estimates points to the methodological aspects to be considered while estimating CHE and impoverishment with limited information about the consumption expenditure by using the capacity-to-pay approach using NSS data [33].CHE and impoverishment among the poorest and poorer households have remained consistently high over NSS-2004, NSS-2014, and NSS-2018 [33].Additionally, health insurance has not reduced CHE and impoverishment among poorest, poorer and middle MPCE quintiles.
Over 75% of the TB patients across all the three rounds belong to these MPCE categories which could explain the similarity between our results compared to Mohanty & Dwivedi (2021) [33].
The strength of our study lies in the use of nationwide populationbased study with multiple time-points, allowing representation and comparison of estimates and thus allowing analysis of trends over the last two decades.We used standardized methods in all these rounds that allow a valid comparison of results.
However, this study has some limitations as well.This study being cross-sectional in nature was not able to capture expenditure for the entire duration of TB treatment which might lead to underestimation of total costs.Self-reporting of illness and costs of care along with responder details could also be potential limitations as observed with any health services utilisation study.The lack of information of clinical profile of TB patients such as drug utilisation, any co-morbidities and TB severity could lead to potential confounding.Our study used hardship financing for measuring the impact of the sale of assets or borrowings on interest, the long-term loss of capital or interest payable was not captured due to paucity of data.We could not use a multidimensional poverty index (MDPI) due to lack of data availability which could underestimate our estimates of CHE and impoverishment among tribal populations with TB.Future studies will need to ensure that these aspects are captured.

Policy recommendations
Our study findings have important policy implications as tribal populations show a substantial percentage of households with TB patients exposed to HCB, CHE, hardship financing, and impoverishment effects compared to other vulnerable social groups [scheduled castes (SC), and other backward castes (OBC)] (Fig. 1, Supplementary Table ).This is important to consider as poverty levels are highest among India's tribal population (81.4%), followed by SC (65.8%), and OBC (58.3%) compared to rest of population (33.3%) [34].With regards to vulnerable populations such as tribal populations, there is increasing need for improving the Direct Benefit Scheme for cash transfer and AB-PMJAY scheme for health insurance coverage in order to offset the OOPE impact of TB among tribal populations in India.Improving the reach of these programmes to tribal areas of the country could be one of ways for reducing the healthcare expenditure impact due to TB.Private hospitals in urban areas need to have better collaboration with government TB units for extending the public TB programs to migrant populations many of whom arrive from tribal areas.

Conclusion
There is a worsening trend among tribal households in India with members suffering from TB being exposed to significant OOPE due to hospitalisation expenditure resulting in catastrophic health expenditure and impoverishment.The authors recommend strongly for the increasing public expenditure on health, improved adherence to Tribal Sub-Plan (TSP) guidelines, earmarking percentage of Ministry of Tribal Affairs (MOTA) allocation for health and better targeting of direct benefit transfer and health insurance schemes along with a healthy private-public partnership for improving access to TB services for tribal populations in the country.

Funding
No funding sources.

Fig. 1 .
Fig. 1.Comparision of healthcare burden, percentage of households exposed to CHE, percentage of households exposed to hardship financing, and percentage of households falling below poverty (poverty headcount ratio) among various social groups due to TB hospitalisation across NSSO rounds (2004-2018).

Table 1
Profile of tuberculosis-affected tribal individuals by their selected socioeconomic and demographic characteristics in India,(NSS 2004(NSS  -2018)).

Table 2
Average Expenditure (in USD) and Health Care Burden of Tuberculosis by selected socioeconomic and demographic characteristics of the tribal patients in India(NSS  2004(NSS   -2018)).

Table 4
15rcentage of households exposed to hardship financing for TB by selected socioeconomic characteristics in India,(NSS 2004(NSS  -2018)).The highest expenditure for hospitalisation was seen in the15-35 years age group where the OOPE rose from 122.13 USD in NSS 60th round (2004) to 311.69 USD in NSS 71st round (2014), falling again to 172.38 USD in NSS 75th round (2018) (Table 2).Overall, OOPE increased from 81.81 USD in NSS 60th round (2004) to 159.97 USD in NSS 75th round

Table 5
Multi-variate logistic regression model for predicting the effect of selected background characteristics of hardship financing among tribal populations with TB in India (2004-2018).

Table 6
Percentage of tribal households exposed to CHE for Tuberculosis by selected socioeconomic characteristics in India,(NSS 2004(NSS  -2018)).
stay resulted in 14.227 times more chances for him to pay CHE (OR = 14.227,CI = 3.153-64.188,p-value = 0.001) than hospital stays of up to 5 days.Those with any healthcare coverage had higher odds to exposure to CHE compared to those without (OR = 1.345, 95% CI 0.404-2.531,pvalue = 0.054) (see Table

Table 7
Multi-variate logistic regression model for predicting the effect of selected background characteristics of Catastrophic Health Expenditure among tribal populations with TB in India (2004-2018).
D.John et al.

Table 9
Impact of health insurance on CHE at 10% threshold using Propensity Score Matching (PSM) among tribal populations with TB (NSS 2014, NSS 2018).